01445nas a2200169 4500008004100000245003500041210003500076300000800111490001000119520096400129100001701093700001901110700002301129700002201152700002001174856008101194 2017 eng d00aICON Loop Carpooling Show Case0 aICON Loop Carpooling Show Case a3100 v101013 aIn this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.1 aNanni, Mirco1 aKotthoff, Lars1 aGuidotti, Riccardo1 aO'Sullivan, Barry1 aPedreschi, Dino uhttps://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=31401359nas a2200205 4500008004100000245004600041210004200087520074100129100002400870700001800894700001500912700001900927700001700946700002300963700002200986700002601008700002001034700002001054856007901074 2017 eng d00aThe Inductive Constraint Programming Loop0 aInductive Constraint Programming Loop3 aConstraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, which we call the inductive constraint programming loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other.1 aBessiere, Christian1 aDe Raedt, Luc1 aGuns, Tias1 aKotthoff, Lars1 aNanni, Mirco1 aNijssen, Siegfried1 aO'Sullivan, Barry1 aPaparrizou, Anastasia1 aPedreschi, Dino1 aSimonis, Helmut uhttps://kdd.isti.cnr.it/publications/inductive-constraint-programming-loop01289nas a2200181 4500008004100000245004600041210004200087300000800129490001000137520075100147100001700898700002300915700002200938700002600960700002000986700002001006856008101026 2017 eng d00aThe Inductive Constraint Programming Loop0 aInductive Constraint Programming Loop a3030 v101013 aConstraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming (ICON) loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other end.1 aNanni, Mirco1 aNijssen, Siegfried1 aO'Sullivan, Barry1 aPaparrizou, Anastasia1 aPedreschi, Dino1 aSimonis, Helmut uhttps://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=30701549nas a2200157 4500008004100000245009100041210006900132520094400201100002401145700001801169700001901187700002301206700002201229700002001251856012001271 2016 eng d00aData Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.0 aData Mining and Constraint Programming Foundations of a CrossDis3 aA successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.
This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. 1 aBessiere, Christian1 aDe Raedt, Luc1 aKotthoff, Lars1 aNijssen, Siegfried1 aO'Sullivan, Barry1 aPedreschi, Dino uhttps://kdd.isti.cnr.it/publications/data-mining-and-constraint-programming-foundations-cross-disciplinary-approach01284nas a2200145 4500008004100000245006500041210006400106260004400170520074700214100001900961700001700980700002300997700002201020856009601042 2015 eng d00aFind Your Way Back: Mobility Profile Mining with Constraints0 aFind Your Way Back Mobility Profile Mining with Constraints aCorkbSpringer International Publishing3 aMobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.1 aKotthoff, Lars1 aNanni, Mirco1 aGuidotti, Riccardo1 aO'Sullivan, Barry uhttps://kdd.isti.cnr.it/publications/find-your-way-back-mobility-profile-mining-constraints